The context influences doctors' support of shared decision-making in cancer care
Bibliographic record
Abstract
Most cancer patients in westernised countries now want all information about their situation, good or bad, and many wish to be involved in decision-making. The attitudes to and use of shared decision-making (SDM) by cancer doctors is not well known. Australian cancer clinicians treating breast, colorectal, gynaecological, haematological, or urological cancer were surveyed to identify their usual approach to decision-making and their comfort with different decision-making styles when discussing treatment with patients. A response rate of 59% resulted in 624 complete surveys, which explored usual practice in discussing participation in decision-making, providing information, and perception of the role patients want to play. Univariate and multivariate analyses were performed to identify predictors of use of SDM. Most cancer doctors (62.4%) reported using SDM and being most comfortable with this approach. Differences were apparent between reported high comfort with SDM and less frequent usual practice. Multivariate analysis showed that specialisation in breast or urological cancers compared to other cancers (AOR 3.02), high caseload of new patients per month (AOR 2.81) and female gender (AOR 1.87) were each independently associated with increased likelihood of use of SDM. Barriers exist to the application of SDM by doctors according to clinical situation and clinician characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".